Monday, 10 Dec 2018
Business

When the number of owners influences the rate of unemployment


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When the number of owners affects the unemployment rate
PHILIPPE HUGUEN / AFP

A study by INSEE shows the link between the density of homeowners at the local level and the unemployment rate in the same place. An unexpected consequence of access to property.

The more landowners there are in a demarcated area, the higher the unemployment rate. The correlation is rather unexpected. Friday, April 20th, Le Figaro relays a study by INSEE that the higher the density of homeowners in a geographic area, the more unemployment increases in that area.

“If the status of owner is generally associated with a better situation on the labor market, an increase in the density of homeowners at the local level yet goes hand in hand with a rise in the unemployment rate,” says INSEE. Precisely, a rise in the density of 10 points would be associated with friction on the labor market increasing by about 1.3 point the local unemployment rate, reports INSEE. Before qualifying: “The resultant of the two effects would nevertheless be of moderate magnitude, up to 0.6 point on local unemployment”.

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A stable job for … stable housing

In the same report, the statistics institute states that “homeowners are actually less often unemployed than tenants: a higher density of homeowners automatically plays a downward role in the unemployment rate”. In fact, it is generally easier to find a home when you have a stable job rather than a precarious contract. Individually, homeowners are therefore less exposed to unemployment than other people.

INSEE tries to explain the link between these two different notions: “A high density of landlords can cause tensions in the housing market: these tensions are likely to complicate the job search of the unemployed, for example by limiting their opportunities to find housing close to job offers “.

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This paradox would be valid throughout France where property has grown considerably over the past fifty years. To reach these conclusions, data from successive censuses of the population between 1968 and 2011 were analyzed.

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